{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* summary: 第二章实验，numpy、pandas、matplotlib应用入门\n",
    "* author: Mr. GAO\n",
    "* date：2022-10-19\n",
    "**********"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 实验内容\n",
    "1. 安装Numpy、Pandas、Matplotlib；\n",
    "2. Numpy构造数组、矩阵运算；\n",
    "3. Pandas之Series、DataFrame的应用；\n",
    "4. 利用Matplotlib绘制折线图、散点图等。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 安装Numpy、Pandas、Matplotlib"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "打开Anaconda3的Anaconda Prompt(miniconda3)在命令行窗口使用pip或conda命令安装，使用“-i”指定安装的源。\n",
    "\n",
    "如： \n",
    "\n",
    "pip install numpy -i https://pypi.douban.com/simple/\n",
    "\n",
    "pip install pandas -i https://pypi.douban.com/simple/\n",
    "\n",
    "pip install Matplotlib -i https://pypi.douban.com/simple/"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Numpy构造数组、矩阵运算"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "numpy（Numerical Python，即数值Python，是Python进行科学计算的一个基础模块。）\n",
    "\n",
    "ndarray即N维数组，是numpy模块的核心数据结构。用多维数组计算，非常便捷高效并且可以节省空间。\n",
    "\n",
    "导入numpy库：import numpy as np，并取了一个更简略的别名，在调用库函数时，通过在函数名前加np.即可调用：np.function_name()。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 导入numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 构造一维数组\n",
    "\n",
    "构造一维数组，并查看数组的维数、元素个数，安装一定规则对数组进行筛选"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "4\n",
      "[2 3 4]\n"
     ]
    }
   ],
   "source": [
    "one_dim_array = np.array([1,2,3,4])\n",
    "print(one_dim_array.ndim)\n",
    "print(one_dim_array.size)\n",
    "filtered_array = one_dim_array[one_dim_array >= 2]\n",
    "print(filtered_array)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 构造二维数组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a is: \n",
      " [[1 2 4 5]\n",
      " [2 4 7 3]\n",
      " [2 5 3 9]]\n",
      "b is: \n",
      " [[0. 0. 0.]\n",
      " [0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([[1,2,4,5],[2,4,7,3],[2,5,3,9]])\n",
    "b = np.zeros([2,3])\n",
    "print(f\"a is: \\n {a}\")\n",
    "print(f\"b is: \\n {b}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 访问array中的元素"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "整个矩阵\n",
      " [[1 2 4 5]\n",
      " [2 4 7 3]\n",
      " [2 5 3 9]]\n",
      "第一行 [1 2 4 5]\n",
      "第一列 [1 2 2]\n",
      "切片 [2 4]\n",
      "切片 [1 2]\n",
      "切片 [1 4]\n",
      "b is: \n",
      " [[False False  True  True]\n",
      " [False  True  True False]\n",
      " [False  True False  True]]\n",
      "c is: \n",
      " [4 5 4 7 5 9]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([[1,2,4,5],[2,4,7,3],[2,5,3,9]])\n",
    "print(\"整个矩阵\\n\", a)\n",
    "print('第一行',a[0])\n",
    "print('第一列',a[:,0])\n",
    "print('切片', a[0][1:3])\n",
    "print('切片', a[0][:-2])\n",
    "print('切片', a[0][::2])\n",
    "b = a>3 #返回 bool数组\n",
    "print(\"b is: \\n\", b)\n",
    "c = a[a>3].copy() #利用bool数组选取元素\n",
    "print(\"c is: \\n\", c)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* array运算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3 5 7] [-1 -1 -1] [ 2  6 12] [0.5 1.  1.5] [0 1 1] [1 4 9] [1 0 1]\n",
      "dim of a = 2\n",
      "shape of a =  (3, 3)\n",
      "max of a = 5\n",
      "sum of a = 28\n",
      "20\n",
      "[17 25 19]\n"
     ]
    }
   ],
   "source": [
    "from numpy import dot, ndim, shape\n",
    "\n",
    "a = np.array([[1,2,4],[2,4,5],[3,5,2]])\n",
    "b = np.array([1,2,3])\n",
    "c = np.array([2,3,4])\n",
    "print(b+c, b-c, b*c, b/2,b//2, b**2,b%2) #常见运算符重载\n",
    "print('dim of a =', ndim(a)) #查看维度\n",
    "print('shape of a = ', shape(a)) #查看大小\n",
    "print('max of a =',a.max())\n",
    "print('sum of a =',a.sum())\n",
    "print(dot(b,c)) #点积\n",
    "print(dot(a,b)) #矩阵乘法"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 矩阵作为一个类对象的调用其方法或属性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A=\n",
      " [[1. 2.]\n",
      " [3. 4.]]\n",
      "转置A.T=\n",
      " [[1. 3.]\n",
      " [2. 4.]]\n",
      "A的逆A.I=\n",
      " [[-2.   1. ]\n",
      " [ 1.5 -0.5]]\n",
      "矩阵乘法A*A =\n",
      " [[ 7. 10.]\n",
      " [15. 22.]]\n"
     ]
    }
   ],
   "source": [
    "A = np.matrix([[1.0,2.0],[3.0,4.0]]) #创建矩阵\n",
    "B = np.matrix(np.arange(1,3))\n",
    "print('A=\\n',A)\n",
    "print('转置A.T=\\n',A.T)\n",
    "print('A的逆A.I=\\n',A.I)\n",
    "print('矩阵乘法A*A =\\n',A*A)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* '5、使用ufunc函数(通函数)\n",
    "\n",
    "numpy中的 ufunc具有向量化特征，且用c语言实现，速度很快"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 2., 0.], dtype=float32)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.linspace(0,np.pi,5)\n",
    "y = np.sin(x)#np.sin(x) 作用到x的每一个元素\n",
    "\n",
    "np.sin(x,x) #第二个参数表示将返回值赋给x本身\n",
    "\n",
    "'frompyfunc函数可将普通python函数转换成ufunc函数'\n",
    "\n",
    "def f(x):\n",
    "    y = x if x>= 0 else 0\n",
    "    return(y)\n",
    "\n",
    "uf = np.frompyfunc(f,1,1)  # 三个参数依次为 pyfun,nin,nout\n",
    "z = uf([1,2,-1])           # 返回结果数据元素类型是 object\n",
    "\n",
    "#使用array对象的 astype方法将其转换成 np.float32\n",
    "z.astype('f4') "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas之Series、DataFrame的应用"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* '1,Series对象'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s is:\n",
      " 0    1.0\n",
      "1    NaN\n",
      "2    6.0\n",
      "3    8.0\n",
      "dtype: float64\n",
      "now s is:\n",
      " one      1.0\n",
      "two      NaN\n",
      "three    6.0\n",
      "four     8.0\n",
      "dtype: float64\n",
      "Index(['one', 'two', 'three', 'four'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "s = pd.Series([1,np.nan,6,8])\n",
    "print('s is:\\n',s)\n",
    "s.index = ['one','two','three','four']\n",
    "print('now s is:\\n',s)\n",
    "print(s.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-10-19</th>\n",
       "      <td>0.677218</td>\n",
       "      <td>-1.693706</td>\n",
       "      <td>-0.073834</td>\n",
       "      <td>0.051406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-20</th>\n",
       "      <td>-0.081635</td>\n",
       "      <td>1.235791</td>\n",
       "      <td>-0.980255</td>\n",
       "      <td>-0.037664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-21</th>\n",
       "      <td>1.181849</td>\n",
       "      <td>0.400928</td>\n",
       "      <td>0.064623</td>\n",
       "      <td>0.054524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-22</th>\n",
       "      <td>-1.251716</td>\n",
       "      <td>0.545157</td>\n",
       "      <td>-0.882469</td>\n",
       "      <td>0.638732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-23</th>\n",
       "      <td>-1.662339</td>\n",
       "      <td>-1.013920</td>\n",
       "      <td>1.523188</td>\n",
       "      <td>-0.943758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-24</th>\n",
       "      <td>0.442672</td>\n",
       "      <td>0.170928</td>\n",
       "      <td>-0.735645</td>\n",
       "      <td>-0.507084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-25</th>\n",
       "      <td>-0.012749</td>\n",
       "      <td>-1.000703</td>\n",
       "      <td>2.271921</td>\n",
       "      <td>1.332754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-26</th>\n",
       "      <td>-0.041368</td>\n",
       "      <td>-1.440374</td>\n",
       "      <td>0.675346</td>\n",
       "      <td>-0.467667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2022-10-19  0.677218 -1.693706 -0.073834  0.051406\n",
       "2022-10-20 -0.081635  1.235791 -0.980255 -0.037664\n",
       "2022-10-21  1.181849  0.400928  0.064623  0.054524\n",
       "2022-10-22 -1.251716  0.545157 -0.882469  0.638732\n",
       "2022-10-23 -1.662339 -1.013920  1.523188 -0.943758\n",
       "2022-10-24  0.442672  0.170928 -0.735645 -0.507084\n",
       "2022-10-25 -0.012749 -1.000703  2.271921  1.332754\n",
       "2022-10-26 -0.041368 -1.440374  0.675346 -0.467667"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'2,用array创建DataFrame对象'\n",
    "dates = pd.date_range('20221019',periods=8)  \n",
    "df = pd.DataFrame(np.random.randn(8,4),\n",
    "                  index=dates,columns=list('ABCD'))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>张三</td>\n",
       "      <td>22</td>\n",
       "      <td>男</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>李四</td>\n",
       "      <td>23</td>\n",
       "      <td>女</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>王五</td>\n",
       "      <td>21</td>\n",
       "      <td>男</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name  age gender\n",
       "0   张三   22      男\n",
       "1   李四   23      女\n",
       "2   王五   21      男"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'3，导入excel文档创建DataFrame对象'\n",
    "classmates = pd.read_excel('classmates.xlsx')\n",
    "classmates.columns = ['name','age','gender']\n",
    "classmates"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 4,增加行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>张三</td>\n",
       "      <td>22</td>\n",
       "      <td>男</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>李四</td>\n",
       "      <td>23</td>\n",
       "      <td>女</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>王五</td>\n",
       "      <td>21</td>\n",
       "      <td>男</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>文文</td>\n",
       "      <td>24</td>\n",
       "      <td>女</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>灵灵</td>\n",
       "      <td>21</td>\n",
       "      <td>女</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name age gender\n",
       "0   张三  22      男\n",
       "1   李四  23      女\n",
       "2   王五  21      男\n",
       "3   文文  24      女\n",
       "4   灵灵  21      女"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classmates.loc[3] = {'name':'文文','age':24,'gender':'女'}\n",
    "\n",
    "# ignore_index=True 表示索引重置\n",
    "classmates = pd.concat([classmates, pd.DataFrame([['灵灵','21','女']], columns=classmates.columns)], ignore_index=True)\n",
    "classmates"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 5,删除行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原来的classmates是：\n",
      "   name age gender\n",
      "0   张三  22      男\n",
      "1   李四  23      女\n",
      "2   王五  21      男\n",
      "3   文文  24      女\n",
      "4   灵灵  21      女\n",
      "现在的classmates是：\n",
      "   name age gender\n",
      "0   张三  22      男\n",
      "1   李四  23      女\n",
      "2   王五  21      男\n",
      "3   文文  24      女\n",
      "4   灵灵  21      女\n",
      "classmates_dropped是：\n",
      "   name age gender\n",
      "0   张三  22      男\n",
      "1   李四  23      女\n",
      "4   灵灵  21      女\n"
     ]
    }
   ],
   "source": [
    "# drop 和 append作用相反\n",
    "# drop并没有直接作用在classmates上，只是将删除后的作为函数的返回而已\n",
    "print(\"原来的classmates是：\\n\", classmates)\n",
    "classmates_droped = classmates.drop([2,3])\n",
    "print(\"现在的classmates是：\\n\", classmates)\n",
    "print(\"classmates_dropped是：\\n\", classmates_droped)\n",
    "# 如果想要删除classmates的第2行、3行，使用语句：classmates = classmates.drop([2,3])\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 6，增加列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>hobby</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>张三</td>\n",
       "      <td>22</td>\n",
       "      <td>男</td>\n",
       "      <td>绘画</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>李四</td>\n",
       "      <td>23</td>\n",
       "      <td>女</td>\n",
       "      <td>书法</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>王五</td>\n",
       "      <td>21</td>\n",
       "      <td>男</td>\n",
       "      <td>篮球</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>文文</td>\n",
       "      <td>24</td>\n",
       "      <td>女</td>\n",
       "      <td>羽毛球</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>灵灵</td>\n",
       "      <td>21</td>\n",
       "      <td>女</td>\n",
       "      <td>游泳</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name age gender hobby\n",
       "0   张三  22      男    绘画\n",
       "1   李四  23      女    书法\n",
       "2   王五  21      男    篮球\n",
       "3   文文  24      女   羽毛球\n",
       "4   灵灵  21      女    游泳"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hobby = ['绘画','书法','篮球', '羽毛球','游泳']  #注意和dataframe的现有行数一直，否则报错\n",
    "classmates['hobby'] = hobby\n",
    "classmates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>height</th>\n",
       "      <th>gender</th>\n",
       "      <th>hobby</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>张三</td>\n",
       "      <td>22</td>\n",
       "      <td>170</td>\n",
       "      <td>男</td>\n",
       "      <td>绘画</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>李四</td>\n",
       "      <td>23</td>\n",
       "      <td>165</td>\n",
       "      <td>女</td>\n",
       "      <td>书法</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>王五</td>\n",
       "      <td>21</td>\n",
       "      <td>180</td>\n",
       "      <td>男</td>\n",
       "      <td>篮球</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>文文</td>\n",
       "      <td>24</td>\n",
       "      <td>160</td>\n",
       "      <td>女</td>\n",
       "      <td>羽毛球</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>灵灵</td>\n",
       "      <td>21</td>\n",
       "      <td>166</td>\n",
       "      <td>女</td>\n",
       "      <td>游泳</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name age  height gender hobby\n",
       "0   张三  22     170      男    绘画\n",
       "1   李四  23     165      女    书法\n",
       "2   王五  21     180      男    篮球\n",
       "3   文文  24     160      女   羽毛球\n",
       "4   灵灵  21     166      女    游泳"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "height = [170,165,180,160,166]\n",
    "classmates.insert(2,'height',height) # 指定位置插入\n",
    "classmates"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 7,删除列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>hobby</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>张三</td>\n",
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       "      <td>绘画</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>李四</td>\n",
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       "      <td>女</td>\n",
       "      <td>书法</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>王五</td>\n",
       "      <td>21</td>\n",
       "      <td>男</td>\n",
       "      <td>篮球</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>文文</td>\n",
       "      <td>24</td>\n",
       "      <td>女</td>\n",
       "      <td>羽毛球</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>灵灵</td>\n",
       "      <td>21</td>\n",
       "      <td>女</td>\n",
       "      <td>游泳</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name age gender hobby\n",
       "0   张三  22      男    绘画\n",
       "1   李四  23      女    书法\n",
       "2   王五  21      男    篮球\n",
       "3   文文  24      女   羽毛球\n",
       "4   灵灵  21      女    游泳"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classmates = classmates.drop(['height'], axis=1)\n",
    "classmates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
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       "      <th>gender</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>张三</td>\n",
       "      <td>22</td>\n",
       "      <td>男</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>李四</td>\n",
       "      <td>23</td>\n",
       "      <td>女</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>王五</td>\n",
       "      <td>21</td>\n",
       "      <td>男</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>文文</td>\n",
       "      <td>24</td>\n",
       "      <td>女</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>灵灵</td>\n",
       "      <td>21</td>\n",
       "      <td>女</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name age gender\n",
       "0   张三  22      男\n",
       "1   李四  23      女\n",
       "2   王五  21      男\n",
       "3   文文  24      女\n",
       "4   灵灵  21      女"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del classmates['hobby']\n",
    "classmates"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 8, 移动列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>张三</td>\n",
       "      <td>男</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>李四</td>\n",
       "      <td>女</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>王五</td>\n",
       "      <td>男</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>文文</td>\n",
       "      <td>女</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>灵灵</td>\n",
       "      <td>女</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name gender age\n",
       "0   张三      男  22\n",
       "1   李四      女  23\n",
       "2   王五      男  21\n",
       "3   文文      女  24\n",
       "4   灵灵      女  21"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender = classmates.pop('gender')   # 先将gender列弹出，也可以用来删除列\n",
    "classmates.insert(1,'gender',gender) # 再用insert指定位置插入\n",
    "classmates"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 9，排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "classmates is: \n",
      "   name gender  age\n",
      "0   张三      男   22\n",
      "1   李四      女   23\n",
      "2   王五      男   21\n",
      "3   文文      女   24\n",
      "4   灵灵      女   21\n",
      "classmates_sorted is: \n",
      "   name gender  age\n",
      "2   王五      男   21\n",
      "4   灵灵      女   21\n",
      "0   张三      男   22\n",
      "1   李四      女   23\n",
      "3   文文      女   24\n"
     ]
    }
   ],
   "source": [
    "classmates['age'] = classmates['age'].astype(int)  # 将整列改成同样的类型才能进行排序\n",
    "classmates_sorted = classmates.sort_values(by='age') # 将排序后的结果返回，但并没有影响原来的dataframe\n",
    "print('classmates is: \\n', classmates)\n",
    "print('classmates_sorted is: \\n', classmates_sorted)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 10, 选取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 3)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classmates.shape #查看形状'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>张三</td>\n",
       "      <td>男</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>李四</td>\n",
       "      <td>女</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>王五</td>\n",
       "      <td>男</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name gender  age\n",
       "0   张三      男   22\n",
       "1   李四      女   23\n",
       "2   王五      男   21"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classmates.head(3) #前3行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>王五</td>\n",
       "      <td>男</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>文文</td>\n",
       "      <td>女</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>灵灵</td>\n",
       "      <td>女</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name gender  age\n",
       "2   王五      男   21\n",
       "3   文文      女   24\n",
       "4   灵灵      女   21"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classmates.tail(3) #后3行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>李四</td>\n",
       "      <td>女</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>王五</td>\n",
       "      <td>男</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name gender  age\n",
       "1   李四      女   23\n",
       "2   王五      男   21"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classmates[1:3] #选择多行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>王五</td>\n",
       "      <td>男</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>文文</td>\n",
       "      <td>女</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name gender  age\n",
       "2   王五      男   21\n",
       "3   文文      女   24"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classmates[2:4] #选择多行\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    张三\n",
       "1    李四\n",
       "2    王五\n",
       "3    文文\n",
       "4    灵灵\n",
       "Name: name, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classmates.name #提取一列返回series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>张三</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>李四</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>王五</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>文文</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>灵灵</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name  age\n",
       "0   张三   22\n",
       "1   李四   23\n",
       "2   王五   21\n",
       "3   文文   24\n",
       "4   灵灵   21"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classmates[['name','age']] #选择多列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>张三</td>\n",
       "      <td>男</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>李四</td>\n",
       "      <td>女</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name gender  age\n",
       "0   张三      男   22\n",
       "1   李四      女   23"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classmates.iloc[0:2,:] #用下标查看数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>张三</td>\n",
       "      <td>男</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>李四</td>\n",
       "      <td>女</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name gender  age\n",
       "0   张三      男   22\n",
       "1   李四      女   23"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#布尔索引\n",
    "classmates[(classmates['age']>21) & (classmates.age<24)]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 11, 常用统计函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.566296</td>\n",
       "      <td>-0.080797</td>\n",
       "      <td>-0.139607</td>\n",
       "      <td>0.176868</td>\n",
       "      <td>-0.035815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.996579</td>\n",
       "      <td>0.931509</td>\n",
       "      <td>1.049428</td>\n",
       "      <td>1.389513</td>\n",
       "      <td>1.323037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.900862</td>\n",
       "      <td>-1.496437</td>\n",
       "      <td>-1.425263</td>\n",
       "      <td>-1.047444</td>\n",
       "      <td>-1.934136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-1.264781</td>\n",
       "      <td>-0.603721</td>\n",
       "      <td>-1.004431</td>\n",
       "      <td>-0.905871</td>\n",
       "      <td>-0.932706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.392702</td>\n",
       "      <td>0.079335</td>\n",
       "      <td>0.080206</td>\n",
       "      <td>-0.208165</td>\n",
       "      <td>0.187778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-0.038974</td>\n",
       "      <td>0.700619</td>\n",
       "      <td>0.427903</td>\n",
       "      <td>0.817643</td>\n",
       "      <td>1.081672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.765029</td>\n",
       "      <td>0.776677</td>\n",
       "      <td>1.249381</td>\n",
       "      <td>2.503841</td>\n",
       "      <td>1.270257</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              A         B         C         D         E\n",
       "count  6.000000  6.000000  6.000000  6.000000  6.000000\n",
       "mean  -0.566296 -0.080797 -0.139607  0.176868 -0.035815\n",
       "std    0.996579  0.931509  1.049428  1.389513  1.323037\n",
       "min   -1.900862 -1.496437 -1.425263 -1.047444 -1.934136\n",
       "25%   -1.264781 -0.603721 -1.004431 -0.905871 -0.932706\n",
       "50%   -0.392702  0.079335  0.080206 -0.208165  0.187778\n",
       "75%   -0.038974  0.700619  0.427903  0.817643  1.081672\n",
       "max    0.765029  0.776677  1.249381  2.503841  1.270257"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "dates = pd.date_range('20221019',periods=6)\n",
    "df = pd.DataFrame(data=np.random.randn(6,5),\n",
    "                  index = dates,columns = list('ABCDE'))\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df.mean():\n",
      " A   -0.566296\n",
      "B   -0.080797\n",
      "C   -0.139607\n",
      "D    0.176868\n",
      "E   -0.035815\n",
      "dtype: float64\n",
      "df.sum(1):\n",
      " 2022-10-19   -1.768896\n",
      "2022-10-20   -3.730866\n",
      "2022-10-21    4.234149\n",
      "2022-10-22   -5.089383\n",
      "2022-10-23   -0.615459\n",
      "2022-10-24    3.096574\n",
      "Freq: D, dtype: float64\n",
      "df.apply(max,1):\n",
      " 2022-10-19    1.249381\n",
      "2022-10-20   -0.349734\n",
      "2022-10-21    2.503841\n",
      "2022-10-22   -0.435669\n",
      "2022-10-23    1.270257\n",
      "2022-10-24    1.020407\n",
      "Freq: D, dtype: float64\n",
      "df.apply(MaxDrawDown):\n",
      " A   -3.014502\n",
      "B   -3.463800\n",
      "C   -2.140775\n",
      "D   -5.773171\n",
      "E   -1.937754\n",
      "dtype: float64\n",
      "formatdf:\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-10-19</th>\n",
       "      <td>-190.09%</td>\n",
       "      <td>60.74%</td>\n",
       "      <td>124.94%</td>\n",
       "      <td>20.94%</td>\n",
       "      <td>-193.41%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-20</th>\n",
       "      <td>-34.97%</td>\n",
       "      <td>-65.54%</td>\n",
       "      <td>-131.46%</td>\n",
       "      <td>-99.93%</td>\n",
       "      <td>-41.18%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-21</th>\n",
       "      <td>76.50%</td>\n",
       "      <td>-44.87%</td>\n",
       "      <td>23.42%</td>\n",
       "      <td>250.38%</td>\n",
       "      <td>117.98%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-22</th>\n",
       "      <td>-43.57%</td>\n",
       "      <td>-149.64%</td>\n",
       "      <td>-142.53%</td>\n",
       "      <td>-62.57%</td>\n",
       "      <td>-110.63%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-23</th>\n",
       "      <td>-154.12%</td>\n",
       "      <td>77.67%</td>\n",
       "      <td>-7.38%</td>\n",
       "      <td>-104.74%</td>\n",
       "      <td>127.03%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-24</th>\n",
       "      <td>6.46%</td>\n",
       "      <td>73.17%</td>\n",
       "      <td>49.25%</td>\n",
       "      <td>102.04%</td>\n",
       "      <td>78.74%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D         E\n",
       "2022-10-19  -190.09%    60.74%   124.94%    20.94%  -193.41%\n",
       "2022-10-20   -34.97%   -65.54%  -131.46%   -99.93%   -41.18%\n",
       "2022-10-21    76.50%   -44.87%    23.42%   250.38%   117.98%\n",
       "2022-10-22   -43.57%  -149.64%  -142.53%   -62.57%  -110.63%\n",
       "2022-10-23  -154.12%    77.67%    -7.38%  -104.74%   127.03%\n",
       "2022-10-24     6.46%    73.17%    49.25%   102.04%    78.74%"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('df.mean():\\n',df.mean()) #求每列平均\n",
    "print('df.sum(1):\\n', df.sum(1)) #按行求和 \n",
    "\n",
    "print('df.apply(max,1):\\n', df.apply(max,1)) #求每行最大值\n",
    "\n",
    "MaxDrawDown = lambda x:min([(x[i]-max(x[0:i+1])+0.0)/max(x[0:i+1]) \\\n",
    "         for i in range(len(x))]) #定义最大回撤函数\n",
    "print('df.apply(MaxDrawDown):\\n',df.apply(MaxDrawDown)) #调用lamda函数\n",
    "\n",
    "formatdf = df.applymap(lambda x:format(x, '.2%')) #转换成百分数显示\n",
    "print('formatdf:\\n')\n",
    "formatdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-10-19</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-20</th>\n",
       "      <td>-0.816013</td>\n",
       "      <td>-2.079071</td>\n",
       "      <td>-2.052235</td>\n",
       "      <td>-5.773171</td>\n",
       "      <td>-0.787074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-21</th>\n",
       "      <td>-3.187458</td>\n",
       "      <td>-0.315376</td>\n",
       "      <td>-1.178154</td>\n",
       "      <td>-3.505677</td>\n",
       "      <td>-3.864719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-22</th>\n",
       "      <td>-1.569481</td>\n",
       "      <td>2.335057</td>\n",
       "      <td>-7.085427</td>\n",
       "      <td>-1.249888</td>\n",
       "      <td>-1.937754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-23</th>\n",
       "      <td>2.537437</td>\n",
       "      <td>-1.519017</td>\n",
       "      <td>-0.948222</td>\n",
       "      <td>0.674086</td>\n",
       "      <td>-2.148169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-24</th>\n",
       "      <td>-1.041925</td>\n",
       "      <td>-0.057906</td>\n",
       "      <td>-7.673306</td>\n",
       "      <td>-1.974188</td>\n",
       "      <td>-0.380139</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D         E\n",
       "2022-10-19  0.000000  0.000000  0.000000  0.000000  0.000000\n",
       "2022-10-20 -0.816013 -2.079071 -2.052235 -5.773171 -0.787074\n",
       "2022-10-21 -3.187458 -0.315376 -1.178154 -3.505677 -3.864719\n",
       "2022-10-22 -1.569481  2.335057 -7.085427 -1.249888 -1.937754\n",
       "2022-10-23  2.537437 -1.519017 -0.948222  0.674086 -2.148169\n",
       "2022-10-24 -1.041925 -0.057906 -7.673306 -1.974188 -0.380139"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfchange = df.pct_change()  #求每日变化比例\n",
    "dfchange.fillna(0)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 利用Matplotlib绘制折线图、散点图等"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 1，折线图—————函数式绘图示范'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, -80, '波峰')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "x = np.linspace(0,2*np.pi,100,endpoint=True)\n",
    "y = np.sin(x)\n",
    "x2 = x;y2 = np.cos(x2)\n",
    "plt.plot(x,y,'b-d',label='中文$sin(x)$')  \n",
    "plt.plot(x2,y2,'r-*',label='$cos(x)$') \n",
    "plt.xlabel('x',fontsize=14);plt.ylabel('y',fontsize=14)\n",
    "plt.grid(color='b' , linewidth='0.3' ,linestyle='--')\n",
    "plt.title('wave graph',fontsize=18,color='r', weight='light' )\n",
    "plt.xlim(min(x)-0.1,max(x)+0.1)\n",
    "plt.ylim(min(y)-0.1,max(y)+0.1)\n",
    "\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n",
    "\n",
    "plt.legend(loc='best',fontsize = 15) ##最优化标签位置\n",
    "plt.annotate('波峰',xy=[np.pi/2,1],xycoords='data',xytext=(-0,-80),\n",
    "             arrowprops=dict(color='red',shrink=0.1),\n",
    "             textcoords='offset points',fontsize = 15,color = 'black')\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1，控制颜色\n",
    "颜色之间的对应关系为 b---blue c---cyan g---green k----black m---magenta r---red w---white y----yellow\n",
    "\n",
    "有四种表示颜色的方式 a:用英文全名如：purple b: 用16进制如：#FF00FF\n",
    "c:用RGB或RGBA元组（1,0,1,1） d：灰度强度如：‘0.7’\n",
    "\n",
    "2，控制线型\n",
    "符号和线型之间的对应关系为\n",
    "\n",
    "实线 -- 短线 -. 短点相间线 ： 虚点线\n",
    "3，控制标记风格\n",
    ". Point marker , Pixel marker o Circle marker + Plus marker v Triangle down marker ^ Triangle up marker < Triangle left marker > Triangle right marker\n",
    "\n",
    "1 Tripod down marker 2 Tripod up marker 3 Tripod left marker 4 Tripod right marker\n",
    "\n",
    "s Square marker p Pentagon marker\n",
    "\n",
    "Star marker x Cross (x) marker h Hexagon marker H Rotated hexagon\n",
    "D Diamond marker d Thin diamond marker\n",
    "| Vertical line marker _ Horizontal line marker\n",
    "r'' any Latex expression"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 1，折线图—————面向对象绘图示范"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x240 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "#设置图片大小和像素\n",
    "myfig = plt.figure(2,figsize=(6,4),dpi=60) \n",
    "#使用图像坐标添加子图相对位置\n",
    "ax1 = myfig.add_axes([0,0.3,0.5,0.5]) \n",
    "# [x坐标起点，y坐标起点，x图宽，y图宽]\n",
    "ax2 = myfig.add_axes([0.6,0.9,0.5,0.5]) \n",
    "x = np.linspace(0,2*np.pi,100,endpoint=True)\n",
    "y = np.sin(x)\n",
    "ax1.plot(x,np.sin(x),label='$sin(x)$')\n",
    "ax1.set_title('正弦曲线',color='red',fontsize =18)\n",
    "ax1.legend(loc='best',fontsize=16)\n",
    "ax2.plot(x,np.cos(x),label='$cos(x)$')\n",
    "ax2.set_title('余弦曲线',color='red',fontsize =18)\n",
    "ax2.legend(loc='best',fontsize=16)\n",
    "myfig.set_facecolor([0,1,0,0.5])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 2,条形图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np;import pandas as pd \n",
    "plt.bar([1,3,5,7,9],[5,2,7,8,2], label=\"Example one\")\n",
    "plt.bar([2,4,6,8,10],[8,6,2,5,6], label=\"Example two\", color='g')\n",
    "plt.xlabel('bar number');plt.ylabel('bar height')\n",
    "plt.title('gragh')\n",
    "plt.legend();"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 3,分布柱状图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "population_ages = [22,55,62,45,21,22,34,42,42,4,99,102,110,\n",
    "    120,121,122,130,111,115,112,80,75,65,54,44,43,42,48]\n",
    "bins = [0,10,20,30,40,50,60,70,80,90,100,110,120,130]\n",
    "plt.hist(population_ages, bins, histtype='bar', rwidth=0.8)\n",
    "plt.xlabel('x');plt.ylabel('y')\n",
    "plt.title('Interesting Graph');"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 4,散点图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [1,2,3,4,5,6,7,8]\n",
    "y = [5,2,4,2,1,4,5,2]\n",
    "\n",
    "plt.scatter(x,y, label='skitscat', color='b', s=25)\n",
    "\n",
    "plt.xlabel('x');plt.ylabel('y')\n",
    "plt.title('Interesting Graph\\nCheck it out')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 5,饼图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "slices = [7,2,2,13]\n",
    "activities = ['睡眠','吃饭','工作','玩']\n",
    "cols = ['c','m','r','b']\n",
    "\n",
    "plt.pie(slices,labels=activities,colors=[(1,1,0),(1, 0, 0), (0, 1, 0), (0, 0, 1)],\n",
    "        startangle=90,shadow= True,explode=(0,0.1,0,0),\n",
    "        autopct='%1.1f%%')\n",
    "\n",
    "plt.title('兴趣爱好');\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 6，绘制子图'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def f(t):return np.exp(-t) * np.cos(2 * np.pi * t)  \n",
    "t1 = np.arange(0, 5, 0.1) ;t2 = np.arange(0, 5, 0.02)   \n",
    "plt.figure(10,figsize=[8,6])  \n",
    "plt.subplot(221)  #分成2x2，占用第一个，即第一行第一列的子图\n",
    "plt.plot(t1, f(t1), 'bo', t2, f(t2), 'r--')  \n",
    "plt.subplot(222)  #分成2x2，占用第二个，即第一行第二列的子图\n",
    "plt.plot(t2, np.cos(2 * np.pi * t2), 'r--')  \n",
    "plt.subplot(212)  #分成2x1，占用第二个，即第二行\n",
    "plt.plot([1, 2, 3, 4], [1, 4, 9, 16])\n",
    "plt.suptitle('Interesting Graph',fontsize = 15)\n",
    "plt.show()"
   ]
  },
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    "# 参考\n",
    "\n",
    "https://github.com/lyhue1991/PythonAiRoad/blob/master/3%E5%B0%8F%E6%97%B6%E5%85%A5%E9%97%A8numpy%2Cpandas%2Cmatplotlib.ipynb"
   ]
  }
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